Method for activating restraining means

ABSTRACT

A method for triggering restraint devices in which at least one collision-indicating signal is generated. From the moment that a collision is detected, temporally defined crash phases are specified, and, for every crash phase, a crash type and a crash severity are determined from the signal. The appropriate restraint devices are triggered as a function of the crash severity and/or the crash type.

PRIORITY APPLICATION INFORMATION

This application claims priority to and the benefit of German Patentapplication no. 102 52 227.8, which was filed in Germany on Nov. 11,2002, the disclosure of which is hereby incorporated by reference.

FIELD OF THE INVENTION

The present invention is based on a method for triggering restraintdevices.

BACKGROUND INFORMATION

German Patent document no. 199 09 538 A1 refers to a method fortriggering restraint devices, in which initially the crash type isdetermined. If a crash type cannot be inferred unequivocally,probability values are used. The method has a modular structure andfeatures an accident classification module, a calculation module and acomparison module.

SUMMARY OF THE INVENTION

By contrast, the exemplary method according to the present invention fortriggering restraint devices having the features of the independentclaim has the advantage that the crash type and also the crash severityare determined for crash phases, which begin with the detection of acollision. This allows for an early decision regarding triggering. Thusaccidents constituting a powerful event, i.e. a severe accident, mayresult already early on in the triggering of restraint devices.Experience shows that the longer a triggering decision or a triggeringof restraint devices is deferred, the more difficult and complex will bethe triggering algorithm. Hence it is practical to be able to makedecisions already early on so as to save time and complexity by virtueof a simpler structure of the algorithm. The simple and structureddesign of the method or algorithm according to the present inventionadditionally allows for the simple integration of new functions.Overall, the exemplary method according to the present invention resultsin better triggering and a more accurately timed triggering of restraintdevices.

The use of crash phases and an associated control of the sensitivitiesof the crash severity detection system allow for the crash severity tobe optimally ascertained in agreement with the collision sensor signals.Furthermore, a crash phase and a crash type detection system forcontrolling the sensitivity of the crash severity algorithms may beprovided. The crash type detection system allows for an adaptedselection of the crash severity algorithm and hence for specific crashseverities for the individual crash severity algorithms. The modularstructure of the algorithm as a whole is thus expandable by additionalmodules. An expansion of the module functionality is provided for by themodular structure itself. If new crash types are added that are to bedetected, if need be, a sub-module may be created for each module. Themethod or algorithm according to the present invention avoids allfeedback. Furthermore, a uniform structure for expansions such as theaddition of upfront sensors, for example, is provided.

Especially advantageous is the fact that the crash phases are defined asa function of the vehicle type. The crash phases in particular depend onexperimental crash tests so as to respond to the deformability of thespecific vehicle type.

The crash type for each crash phase is determined particularly by thefact that for different crash types—frontal collision, offset collision,side collision, rear collision, crash into a deformable barrier or acrash into a post—the at least one signal from the collision sensor isanalyzed and the results of these analyses are combined with one anotherto determine the crash type. Thus, the collision signal can be analyzedin parallel for the various possible crash types so as to determinewhich crash type is the suitable one. If no clear classificationresults, calculations can be based on probabilities. Thus a weightedcombination of various crash types is performed. The crash type thusamalgamated then determines whether one or more algorithms are used todetermine the crash severity. If the crash type, for example, cannot beclassified unequivocally, multiple algorithms are used to determine thecrash severity so as to form a weighted sum of the crash severity inthis case as well.

It is furthermore advantageous that the triggering of the restraintdevices is in the end carried out only as a function of a plausibilitysignal. This plausibility signal is also derived from the at least onesignal of the collision sensor. This results in an increased reliabilityof the method according to the present invention.

In addition it is advantageous that, for each of the various collisionsensors in the vehicle, the crash severity is ascertained separately forevery crash phase in the manner described above. The resulting crashtypes and crash severities for the individual collision sensors are thencombined with one another to ascertain in each case one crash type andone crash severity. Another possibility for determining the suitablecrash type or crash severity lies in the meaningful combination ofdifferent types of sensor signals, which in a joint evaluation yield acrash type or crash severity. Such diverse collision sensors include thesensors in the central unit, for example on the vehicle tunnel, andupfront sensors, i.e. in particular those mounted on the radiator, andalso side collision sensors. Deformation sensors, indirect deformationsensors such as pressure and temperature sensors and pre-crash sensorsmay also be used as collision sensors, in addition to the usualacceleration sensors. A refinement provides for the derivation of aweighting factor as a function of the particular crash type or crashseverity ascertained, the weighting factor being used for the individualcrash type for the respective collision sensor. This makes it possibleto assign, during a frontal collision for example, a higher weight onthe upfront sensor with respect to the crash types or crash severityascertained here than to the sensor in the central unit. This allows fora more precise determination of the crash type or the crash severity andthus for better triggering of the restraint devices. This evaluation maybe continuous, i.e. using weighting factors representing a series ofnumbers, or also discrete, in which case a threshold is used to decidewhether, for example, the values of a collision sensor are used at allor are weighted very heavily.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 shows a block diagram of the exemplary device according to thepresent invention.

FIG. 2 shows a flowchart of the exemplary method according to thepresent invention.

FIG. 3 shows a first block diagram of the exemplary method according tothe present invention

FIG. 4 shows a second block diagram of the exemplary method according tothe present invention.

FIG. 5 shows a third block diagram of the exemplary method according tothe present invention.

FIG. 6 shows a fourth block diagram of the exemplary method according tothe present invention.

FIG. 7 shows a diagram for determining the weighting.

FIG. 8 shows a fifth block diagram of the exemplary method according tothe present invention.

FIG. 9 shows a sixth block diagram of the method according to thepresent invention.

DETAILED DESCRIPTION

The following describes a method for triggering restraint devices, whichis particularly distinguished by the fact that it features no feedbackand that it operates with fixed thresholds. It is a distinguishingfeature that here various characteristics are extracted from theacceleration values, for example a windowed integral of theacceleration, i.e. a speed value.

Furthermore, a new feature of the method according to the presentinvention is the use of crash phases. As shown above, this allows for adecision already to be made early on regarding some types of accidentsor crash severities so that subsequently only the other types ofaccidents and crash severities requiring a later decision remain to beprocessed using a more complex algorithm. As shown above, the crashphases depend on the vehicle type.

If, when employing a windowed integral, variable window lengths are usedin the extraction of characteristics, they can be controlled with theaid of time sequences without the use of a timer or a counter. The crashphases are controlled via such implicit times. Within the individualcrash phases, the individual thresholds may be applied in various waysusing the characteristics to reach a decision, e.g. regarding crashtypes. Due to the fact that no timer is used, such a timer can also notinfluence the algorithm negatively due to faulty or false signals, i.e.a misuse.

The results of the preceding blocks can be summarized within the logicgap of the algorithm. Due to the structure, it is possible to processsequences in parallel within the preceding blocks. In the crash-typedetection, the crash types thus may all be computed in parallel and arethen amalgamated via a logic. Due to this parallelization, it is alsoreadily possible to expand the algorithm or the method by an additionalfunctionality, simply by adding more new blocks. Within the logic, dataregarding the quality of the signals or regarding their priorities areprocessed. This can also be interpreted as importance and thusrepresents an important feature. However, other types of data could beprocessed. Only similar information, whether, for example, a specificcrash type was ascertained or not, is processed in the logic blocks.

Thus the algorithm is not based on the separation of signals into anacceleration path and an integrator path. The algorithm uses integratedsignals exclusively. Using suitable methods, characteristics are thenextracted from these signals, which are evaluated via applicablethresholds and which yield information regarding the crash type, i.e.the type of collision, and/or the crash severity, i.e., the severity ofthe collision. The algorithm is modularly structured in such a way thatnew types of collision or severities of collision are easily integrated.

FIG. 1 shows the apparatus according to the present invention in a blockdiagram. Two upfront sensors 1 and 2 mounted on the radiator of avehicle are connected to a control unit 5 via lines. Upfront sensors 1and 2 provide acceleration signals as digital signals to control unit 5.These digital signals are processed by a processor 6 in control unit 5.Processor 6 is also connected to an acceleration sensor 7 in controlunit 5. Processor 6 also processes the signal of this accelerationsensor, which is for example connected to an analog input of processor6. Control unit 5 is furthermore connected to a side-impact sensorsystem 3. Side-impact sensors may be acceleration sensors or deformationsensors or indirect deformation sensors such as pressure or temperaturesensors. Surround sensors may be used here as well. Surround sensors 4,however, are also connected to control unit 5 and thereby to processor6. The type of sensors especially used as pre-crash sensors may also beused to determine a collision signal. Sensor system 7 in control unit 5is usually an acceleration sensor, the acceleration sensor beingconfigurable to have a sensitivity in the X direction and in the Ydirection. Control unit 5 and hence processor 6 are connected torestraint devices 8 via a data output. These restraint devices 8 includeseatbelt tensioners, airbags, active seats and possibly rollover bars.Restraint devices 8 can be triggered individually. For the sake ofsimplicity, an occupant classification system is not shown. The signalsfrom this occupant classification system also influence the triggeringof restraint devices 8.

Using various flowcharts and block diagrams, the operating sequence ofthe method according to the present invention, including the apparatusaccording to the present invention, is shown below. The method accordingto the present invention is shown in terms of its operating sequence ina flowchart in FIG. 2. The appropriate crash phase is set in method step201. For this crash phase, parallel analyses are performed in methodsteps 202, 203 and 204 to determine whether a specific crash type hasoccurred. That is to say, the sensor signals are analyzed as to whetherthey identify a crash type, for example crash type 1 in 202, crash type2 in 203 or crash type 3 in 204. For the sake of simplicity, only threecrash types are represented, while many more than three crash types arepossible. Crash types include frontal collisions, rear collisions,offset collisions, crashes into a deformable barrier, side collisions,crashes into a post, hard crashes and rollover events. The directionascertained from the collision signals, for example, may provide anindication of the crash type. If a frontal collision has occurred, avery strong signal will be measurable in the X direction. If a rearcollision has occurred, a very strong signal will also be measurable inthe X direction, but in the opposite direction. In the case of offsetcrashes or side collisions, the appropriate methods are to be applied toidentify each crash type based on the direction. In the case of a crashinto a deformable barrier, the time characteristic will provide theindication of this crash type.

Non-triggers (AZT (Allianz Zentrum fuer Technik=Alliance Center forTechnology), misuse) can be detected by characteristics in the signalitself. A truck underride on the part of a passenger car is to beclassified via the temporal occurrence of signals, in this case of theupfront sensors and of the central control unit.

Through the use of two upfront sensors, offset crashes may be morefinely classified, for example into crashes, in which the other party tothe collision strikes the vehicle at an angle (angle crash) or whetherthe other party to the collision strikes at a degree of overlap smallerthan 100%. Side crashes can be classified via the temporal occurrenceand the pattern of sensor signals. An example here is the post into thedoor, which can be recognized as a crash type via the pressure signal,if a pressure sensor for sensing a side collision exists, and a suitableplausibility, and can be translated into a crash severity.

In subsequent method step 205, the results of individual method steps202, 203 and 204 are amalgamated. This means that the actual crash typeis now identified from the individual results of the crash-typecomparisons in method step 205. If none of the three crash typesdominate, then a mixed form is generated in method step 205 viaweighting factors or probability values. One or more algorithms fordetermining the crash severity are selected and activated as a functionof the crash type ascertained. If crash type 1, for example, wasunequivocally identified as the crash type in method step 205, thenthere is a jump to method step 206 to process the algorithm for crashseverity 1, since the latter is unequivocally associated with crash type1. The equivalent also holds true for crash severity 2 in method step207 and crash severity 3 in method step 208. In the case of a mixed formof crash types, then at least two crash-severity algorithms areactivated. Even in the case of an unequivocal identification of a crashtype, however, it is possible that at least two algorithms fordetermining the crash severity are processed, which are then amalgamatedin method step 209. This amalgamation is also performed by a weightedsum. As a function of the crash type ascertained from method step 205and the crash severity ascertained from method step 209, restraintdevices 8 are triggered in method step 210. In this context, however, aplausibility check is still performed in method step 211 using thesensor signals to determine whether the restraint devices should betriggered at all.

FIG. 3 shows the method according to the present invention in a blockdiagram. In block 301, collision signals, which may be accelerationsignals, are generated from sensors 1, 2, 3, 4 and/or 7. Thisinformation is subsequently made available to the method according tothe present invention. In block 302, various characteristics areextracted from the acceleration signals, for example throughintegration, differentiation or other complex mathematicaltransformations such as filters, a Kalman filter for instance, or startor stop conditions for a time meter. The following occurs in block 303:From specific characteristics provided by the previous block 302,temporal information may be derived which makes it possible to divide acollision or an impact into different crash phases. The crash phasestemporally succeed one another from the beginning of the crash until theend of the crash. The start of the algorithm is defined as the beginningof the crash, while the algorithm reset is decisive as regards the endpoint. The start of an algorithm is established for example by the factthat a specified noise threshold is exceeded. The number of crash phasesis initially not limited. The crash phase can be represented by a timemeter using the unit cycles, in which the cycles may represent theperiod between the individual computational steps of the algorithm,possibly also shorter or longer periods. This representation in a timemeter corresponds to the maximum number of crash phases. The individualcrash phases are used to control different sensitivities in thecrash-type detection, that is, in block 304 and/or in the crash-severitydetection in block 306. What is meant by sensitivities are thresholdvalues of different magnitudes, with which the individualcharacteristics are compared so as to generate a decision or bring aboutthe triggering of the restraint devices.

In block 304 of the crash-type detection, a classification of the typesof collision is performed using the characteristics from block 302.These different classes may be derived from common crash tests. Here,crashes such as striking a deformable barrier with a degree of overlapof <100%, collisions in which the activation of an triggeringarrangement or structure is not appropriate, driving maneuvers whichfeign a collision due to the acceleration signal (misuse), collisions inwhich the other party involved in the collision stands or moves at anangle to the direction of travel of the first vehicle, collisions whichhave a degree of overlap close to 100% may be classified. Not all crashtypes have to be classifiable within the individual crash phases.However, in individual crash phases, all relevant collision types may beascertained independently of one another. Possible dependencies are nottaken into account here. It is quite possible that several crash typesare detected simultaneously. The results of the crash-type detection arecombined in the following block 305. This block 305 represents acrash-type logic. Here the probabilities of the individual crash typesdetected are evaluated or determined. From this result, thesensitivities of crash-value detection 306 are influenced. Dependenciesof crash types are processed in the crash-type logic in such a way thatthe most probable case is selected via a logic. It may well be the casein a real crash scenario that no unequivocal classification into onecrash type class can be made. Examples for a combination of crash typesare: ODB (impact onto a deformable barrier) and AZT (=insuranceaccident, in which the least possible damage occurs to the vehicle)detected. If the AZT crash prevails and only its correspondingcrash-severity detection is activated, then a crash severity in the caseof the AZT is 0. If no unequivocal decision can be made, it is possibleto choose multiple sensitivities in the crash-severity detection and toevaluate them subsequently in the crash-severity logic. In block 306,i.e. in the crash-severity detection, crash severities are derived basedon the characteristics of the sensor signals. These crash severities arethen processed further within the firing logic, and the restraint deviceappropriate for this collision is activated so as to provide theoccupants with optimal protection. In this context, the number of crashseverities is not limited to the number of restraint devices. Within thecrash-severity detection, paths having different sensitivities of thecrash-severity determination may be activated as a function of thecrash-type logic. The crash severity is determined in a linearlyascending fashion, the smallest crash severity corresponding to acollision in which no restraint device is to be activated. The greatestcrash severity corresponds to the maximum protection to be activated. Inthis case, there is no temporal control of ignitors.

In block 307 of the crash-severity logic, different crash severitiescoming from preceding block 306 are combined in such a way that thevehicle occupants receive protection that is adapted to the situation.In the simplest case, this can be a priority according to the order ofmagnitude of the various crash severities. However, more complex logicalinterconnections may be implemented as well.

In block 309 of the firing logic, the crash severity and/or possibly thecrash type transmitted in this block are assigned the appropriateignitors, and the synchronized activation is also ensured. Here there isalso the possibility of an activation as a function of conditions withinthe vehicle. The states concerned here may be, for example, the positionof the occupant, his/her weight, the status of the seatbelt. Thisapplies to all permissible sitting positions in the vehicle.

Block 308 represents the plausibility check. Within this block 308,possible errors and discrepancies within the algorithm path aremitigated in their effect. For an activation of restraint devices tooccur, the decision should be independently confirmed. A faulty sensorcould result in a triggering, which is why the plausibility should beconfirmed by another sensor, since here the probability that 2 sensorsare simultaneously defective is significantly lower than in the case ofone faulty sensor. This is why an incorrectly functioningmicrocontroller, which performs the calculation and evaluations, couldnot cause a triggering. Additional error scenarios could be reduced intheir effect by this plausibility.

FIG. 4 shows in a block diagram that a crash-phase control 401 triggersindividual crash phases 402, 403 and 404.

Here temporal data are extracted from the sensor signals and aretranslated into individual crash phases. Such an extraction may also beachieved via a timer or counter which is started and stopped as afunction of the signal. Control 401 then takes on the task of switchingindividual crash phases 402-404, or possibly even more, to active. Thisoccurs as a function of the temporal data from the sensor signals or thetimer. Only one crash phase may be switched to active at any one time.Here the crash phases are graded in an ascending order and aresuccessively activated in a crash scenario. This occurs either in afixed manner via a counter or as a function of the sensor signals.

FIG. 5 shows the method according to the present invention in anotherblock diagram. As shown above, for a given crash phase 501, an analysisis performed concurrently, and specifically in blocks 503, 504 and 505,to ascertain which crash type applies. This is determined in method step502 of the crash-type logic using the results of these individualanalyses. As a function of the determined crash type or of a weightedsum of various crash types, the crash-type logic will then select atleast one of crash-severity algorithms 506, 507 or 508 to ascertain thecrash severity from the sensor signals.

FIG. 6 shows an expansion of the method of the present invention. Inblock 601, the crash severity algorithm, as shown in FIG. 3, is runusing the crash-type detection. This is supplied to crash-type logic604, which is here also connected to a corresponding crash-typealgorithm for upfront sensors 602. Thus, the crash type here isgenerated from a signal from a central sensor, which in method step 601yields a corresponding crash type, and the upfront sensors from methodstep 602. In the process, as shown above, the individual crash-typeresults of the central sensor or of the upfront sensors may be suitablyreinforced or weakened. In place of, or in addition to, the upfrontsensors, other sensors such as side-impact sensors or surround sensorsmay be used here as well. The crash-type logic, which thus determinesthe crash type, is connected to the subsequent crash-severity detection605. As shown above, the crash severity is determined from the sensorsignals using at least one algorithm. In method step 606, this result isthen combined via a crash-severity logic with a crash-severity detectionof upfront algorithm 603. On this basis the crash severity is thendetermined which is used for triggering firing logic 607. Other sensormodules such as side-impact sensors or surround sensors may be used hereas well.

The approach to amalgamation is based here on the evaluation of thequality of the individual crash-type or crash-severity data. Differentalgorithms, especially if they access different sensor data, providecrash-type and crash-severity data of different quality. While onealgorithm may be more precise in ascertaining crash type A, anotheralgorithm will have advantages in determining another crash type. In theamalgamation of the information, these differences in quality are nowtaken into account using an appropriate weighting. Since the logic isimplemented in software, it may be modified or expanded through simplereprogramming. The evaluation of the quality of the crash-types andcrash-severity decisions of the various algorithms is performed withsimulations using real or simulated crash test data, i.e., accelerationdata.

An optimally functioning upfront algorithm, for example, is bettersuited for detecting the degree of overlap than a central algorithmbased on a central sensor. Thus, in determining crash types and crashseverities depending on the result of the detection of the degree ofoverlap, the information of the upfront algorithm may be weighted morestrongly than the information of the central crash-severity algorithm.On the other hand, in the case of crash types in which the centralcrash-severity algorithm allows for more precise information, it mayaccordingly be weighted more strongly than the upfront algorithm. Everycrash phase has an independent logic for determining crash type andcrash severity. This feature may be exploited advantageously in theamalgamation with the information of the other algorithms. The qualityof the data from different algorithms generally changes in the course ofa crash. The upfront algorithm, for example, which is based on a sensorsystem in the front end of the vehicle, provides useful additional datato the central crash-severity algorithm only up into the range of themiddle crash phases. In late crash phases, by contrast, there is littleadditional informational content. A destruction may be assumed in thiscase. In the amalgamation, the crash-type and crash-severity data of thevarious algorithms may be weighted differently in each phase inaccordance with their quality at that time.

The method described up to this point may be improved by using atheoretical probability approach in the amalgamation of crash severityand crash type. In practice, the central control unit is normallysubordinated in its importance to the upfront sensor system. In somecrash situations, however, this is not always correct. On the basis ofthe present invention, the information provided by the various sensorsis to be appropriately amalgamated according to its importance. A crashtriggering individually adapted to each particular crash may thereby beachieved. Furthermore, the approach is chosen such that a simpleamalgamation of additional sensors may be readily integrated into thedesign. The flexibility of the algorithm may be expanded accordingly viaa parameter setting or a specific calculation of the importance.

The approach to amalgamation, based on a probability, is first explainedin general terms. A datum directly obtained or derived from the sensorsystem may be transformed via a ramp function into a measure ofprobability. The ramp function may be described by a lower and an upperlimit or threshold and their linear correlation. This correlation isrepresented in FIG. 7. The information to be evaluated, for example thecrash type or the crash severity, is plotted on abscissa 701. Theweighting or the measure of probability is plotted in %, for example, onordinate 707. The ramp function has a lower limit 703, an upper limit702 and a gradient 705. Thus, crash type 704 is allocated to probabilitymeasure 708. The upper limit 702 corresponds to 100% 706. The lowerlimit 703 corresponds to 0%.

As described above, the principle of the ramp function may be used torepresent a datum to be processed as a percentage measure or may be usedas a function for calculating weighting factors.

The respective limits are set via an application. The advantage in thismethod is the high flexibility. On the one hand, a function may besuppressed in that the lower and upper limits are designed in such a waythat given a specified input value, an output value can never be reachedby the processing rule, i.e. the output of the measure is thus 0 oramounts to 0% and is hence invalid for additional functions. On theother hand, due to the continuous output, the output may be convertedinto a binary output by setting the upper and lower limits to an averagevalue, e.g. the value 50. This yields only 2 states, either 0 or 100.This set-up is performed separately for each characteristic. Thefollowing quantities, for example, for the crash-type and crash-severityinformation for the various sensors are thereby obtained.

The individual data are obtained from blocks 1.1 a through 1.2 b in FIG.8. The information applicable to the respective sensor is nowamalgamated in the logic (block 2.1 and block 2.2) as follows:

${ZBS} = \frac{{{CRST}*{Factor}\; 1} + {{CSST}*{Factor}\; 2}}{\left( {{{Factor}\; 1} + {{Factor}\; 2}} \right)}$

${UFS} = \frac{{{CRUFS}*{Factor}\; 3} + {{CSUFS}*{Factor}\; 4}}{\left( {{{Factor}\; 3} + {{Factor}\; 4}} \right)}$

Factors i (here i=1

4) may either be likewise calculated via other characteristics andcorresponding ramp functions, or they are permanently set parameters inthe EEPROM. Hence initially a specific amalgamation of the individualdata from the central control unit and the upfront sensor system iscarried out separately. If required, the information thus obtained maybe transmitted to other modules. Centrally, however, in order to arriveat a triggering decision, these data are combined in a separateevaluation logic (block 3) using the same schema.

${Total} = \frac{{{ZBS}*{Factor}\; 5} + {{UFS}*{Factor}\; 6}}{\left( {{{Factor}\; 5} + {{Factor}\; 6}} \right)}$

As above, the factors may be calculated or may be preset values.Subsequently, this information is processed accordingly by a triggeringlogic in block 4.

Individual values such as CRST, for example, may of course in turn begenerated according to the method described above. This means that thecrash severity algorithm, which itself is able to detect different crashtypes, is able to make these more precise using a weighted sum. If sucha determination is also performed for the crash type of the upfrontsensor information (CRST), then it is possible to combine thisinformation. This also applies to information such as CSST and CSUFS.

This then results in the following alternative of determination:

$\begin{matrix}{{CRTotal} = \frac{{{CRST}*{Factor}\; 6} + {{CRUFS}*{Factor}\; 7}}{\left( {{{Factor}\; 6} + {{Factor}\; 7}} \right)}} \\{{CRTotal} = \frac{{{CRST}*{Factor}\; 8} + {{CRUFS}*{Factor}\; 9}}{\left( {{{Factor}\; 8} + {{Factor}\; 9}} \right)}}\end{matrix}$

In the above computational rule, of course, CRST may in turn have beengenerated from the individual crash types via a weighted sum. The sameapplies to CRUFS, CRST and CRUFS.

EXAMPLE

$\begin{matrix}{{CRTotal} = \frac{\begin{matrix}{{{CRST\_}1*{Fak}\; 10} + {{CRST\_}2*{Fak}\; 11} + \ldots +} \\{{{CRUFS\_}1*{Fak}\; 20} + {{CRUFS\_}1*{Fak}\; 21} + \ldots}\end{matrix}}{\left( {\Sigma\;{Faktoren}} \right)}} \\{{CRTotal} = \frac{\begin{matrix}{{{CRST\_}1*{Fac}\; 10} + {{CRST\_}2*{Fac}\; 11} + \ldots +} \\{{{CRUFS\_}1*{Fac}\; 20} + {{CRUFS\_}1*{Fac}\; 21} + \ldots}\end{matrix}}{\left( {\Sigma\;{Factors}} \right)}}\end{matrix}$

Here, _1 represents the various crash types or crash severities, theprefix indicating the source of the signals.

As can be seen in FIG. 9, the data from blocks 1.1 a through 1.2 b arecombined in deviation from the basic idea. Block 2 contains thecombinations of similar data and block 3 brings these data together tomake them available in block 4.

1. A method for triggering restraint devices, in which at least onecollision-indicating signal is generated, the method comprising: from amoment that a collision is detected, specifying temporally defined crashphases; for every crash phase, a crash type and a crash severity beingdetermined from the signal; and triggering appropriate ones of therestraint devices as a function of the crash severity and the crashtype.
 2. The method of claim 1, wherein the crash phases are defined asa function of the vehicle type.
 3. The method of claim 1, wherein thecrash type is determined for every crash phase and for differentpossible crash types, the at least one signal is analyzed, and resultsof the analyses are combined with one another to determine the crashtype.
 4. The method of claim 3, wherein, to determine the crashseverity, at least one algorithm is selected as a function of the crashtype, and results from the at least one algorithm are used to determinethe crash severity.
 5. The method of claim 1, wherein, from the at leastone signal, a plausibility signal is derived and used to check thetriggering.
 6. The method of claim 1, wherein, for different collisionsensors in the vehicle, the crash type and the crash severity are ineach case determined separately for a respective crash phase, and thecrash types and crash seventies determined in this manner are in eachcase combined for use with the triggering.
 7. The method of claim 6,wherein, for the combining, the respective crash types and crashseventies determined for the different collision sensors are evaluatedas a function of at least one of he relevant crash type, the relevantcrash severity and the relevant collision sensor.
 8. The method of claim7, wherein the evaluation is performed continuously.
 9. The method ofclaim 7, wherein the evaluation is performed with specific thresholds.10. An apparatus for triggering restraint devices, in which at least onecollision-indicating signal is generated, comprising: a specifyingarrangement in which, from a moment that a collision is detected, tospecify temporally defined crash phases; for every crash phase, a crashtype and a crash severity being determined from the signal; and atriggering arrangement to trigger appropriate ones of the restraintdevices as a function of the crash severity and the crash type.
 11. Theapparatus of claim 10, wherein the crash phases are defined as afunction of the vehicle type.
 12. The apparatus of claim 10, wherein thecrash type is determined for every crash phase and for differentpossible crash types, the at least one signal is analyzed, and resultsof the analyses are combined with one another to determine the crashtype.
 13. The apparatus of claim 12, wherein, to determine the crashseverity, at least one algorithm is selected as a function of the crashtype, and results from the at least one algorithm are used to determinethe crash severity.
 14. The apparatus of claim 10, wherein, from the atleast one signal, a plausibility signal is derived and used to check thetriggering.
 15. The apparatus of claim 10, wherein, for differentcollision sensors in the vehicle, the crash type and the crash severityare in each case determined separately for a respective crash phase, andthe crash types and crash severities determined in this manner are ineach case combined for use with the triggering.
 16. The apparatus ofclaim 15, wherein, for the combining, the respective crash types andcrash severities determined for the different collision sensors areevaluated as a function of at least one of he relevant crash type, therelevant crash severity and the relevant collision sensor.
 17. Theapparatus of claim 16, wherein the evaluation is performed continuously.18. The apparatus of claim 16, wherein the evaluation is performed withspecific thresholds.